Recommender Systems (RS) are a commonly
used filtering mechanism to cope with the information overload problem – i.e., automatically
providing relevant information out of very large information spaces by
considering the context (e.g. user preferences, task, history etc.). Primarily,
RS are used to provide results in form of text, or list items, but recently,
they were also used for recommending other kinds of information – the visualizations.

We provide a Recommendation Dashboard
(RD) that includes several interactive visualizations for exploring and
analyzing results of a Recommender System (RS). The RD generates the visualizations
automatically. To avoid providing user with junk visuals the RS tracks user
behavior, and provides visualizations that best match to that behavior. Thus, the
central component of the RS is a user model, or profile – a collection of
parameters that must be tracked in order to feed RS with enough information about
the interactions and in the end to build a user profile.

Student Tasks
The student has to implement an algorithm which tracks and collects users’
interaction with the visualizations and which defines behavioral patterns based
on collected data. The patterns will be used to infer users’ next action using a
content-based RS and in turn to recommend visualizations which address users’
task in the best possible way.